Robust model prediction control method and device based on inductance observation

Abstract

The invention provides a robust model prediction control method and device based on inductance observation, and relates to the technical field of power control, and the method comprises the steps: observing an inductance current error in real time through an inductance observer, and calculating an observation inductance parameter; correcting a system prediction model according to the observation inductance parameters; and calculating according to the system prediction model to obtain the optimal voltage vector output of the power system. According to the method, the inductance observer is constructed based on the control error model, the prediction model parameters are observed and corrected, prediction model steady-state errors caused by inductance parameter mismatch are eliminated, and the stability of the power system is improved.

Description

Technical Field

[0001] The present application relates to the field of power control technology, and in particular to a robust model predictive control method and device based on inductance observation. Background Art

[0002] The control accuracy of model prediction depends largely on the accuracy of the prediction model. Changes in model parameters will cause changes in the prediction model, thereby reducing the performance of model predictive control (MPC). However, the Vienna rectifier under actual operating conditions is affected by many factors such as temperature and current, and the actual inductance parameters will change. If the inductance parameters of the prediction model do not match the actual inductance parameters, the control accuracy of the prediction model will decrease, thereby reducing the quality of the input current. Summary of the invention

[0003] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0004] To this end, the first objective of the present application is to propose a robust model predictive control method based on inductance observation.

[0005] The second object of the present application is to provide a device.

[0006] The third objective of the present application is to provide an electronic device.

[0007] A fourth objective of the present application is to provide a computer-readable storage medium.

[0008] A fifth object of the present application is to provide a computer program product.

[0009] To achieve the above objectives, the first embodiment of the present application proposes a robust model predictive control method based on inductance observation, comprising:

[0010] The inductor current error is observed in real time by using an inductor observer and the observed inductor parameters are calculated;

[0011] Correcting the system prediction model according to the observed inductance parameter;

[0012] The optimal voltage vector output of the power system is calculated according to the system prediction model.

[0013] Optionally, the using an inductor observer to observe the inductor current error in real time and calculate the observed inductor parameter includes:

[0014] The actual inductance value and the ideal inductance value of the power system are obtained. The resistance in the prediction model is the same as the actual resistance of the power system. The actual current prediction value of the next control cycle is calculated according to the actual inductance value, the control cycle length, the voltage measurement value, the current measurement value, and the optimal voltage vector. The formula is:

[0015]

[0016] Among them, i α m (k+1) is the α component of the actual current prediction value, L m is the actual inductance value, T is the control cycle length, e α (k) is the α component of the voltage measurement value, i α (k) is the α component of the current measurement value, u α (k) is the α component of the optimal voltage vector; i β m (k+1) is the β component of the actual current prediction value, L m is the actual inductance value, T is the control cycle length, e β (k) is the β component of the voltage measurement value, i β (k) is the β component of the current measurement value, u β (k) is the β component of the optimal voltage vector;

[0017] The ideal current prediction value of the next control cycle is calculated according to the ideal inductance value, the control cycle length, the voltage measurement value, the current measurement value, and the optimal voltage vector, and the formula is:

[0018]

[0019] Among them, i α (k+1) is the α component of the ideal current prediction value, L is the ideal inductance value, T is the control cycle length, R is the filter resistance, e α (k) is the α component of the voltage measurement value, i α (k) is the α component of the current measurement value, u α (k) is the α component of the optimal voltage vector; i β (k+1) is the β component of the ideal current prediction value, L is the ideal inductance value, T is the control cycle length, e β (k) is the β component of the voltage measurement value, i β (k) is the β component of the current measurement value, u β (k) is the β component of the optimal voltage vector;

[0020] Divide the expressions of α component and β component correspondingly to obtain the observed value L of the inductance parameter at the next moment o for:

[0021]

[0022] Optionally, the method further includes:

[0023] Calculate the inductance observation value at the current moment, the formula is as follows:

[0024]

[0025] Optionally, the correcting the system prediction model according to the observed inductance parameter includes:

[0026] The inductance observation value at the current moment is processed by a limiter and low-pass filtered to obtain a target inductance observation value;

[0027] The target inductance observation value is used to update the inductance observation value in the system prediction model.

[0028] Optionally, calculating the optimal voltage vector output of the power system according to the system prediction model includes:

[0029] The ideal current prediction value of the next control cycle is calculated according to the target inductance observation value, the control cycle length, the voltage measurement value, the current measurement value, and the optimal voltage vector. The formula is:

[0030]

[0031] L b is the target inductance observation value.

[0032] Optionally, calculating the optimal voltage vector output of the power system according to the system prediction model includes:

[0033] According to the ideal current prediction value and actual current value of the next control cycle, the objective function with the current control error as the constraint term is determined:

[0034] E i =[i α m (k+1)-i α (k+1)] 2 +[i β m (k+1)-i β (k+1)] 2

[0035] Among them, E i To control the error;

[0036] calculating a duty cycle according to the control error;

[0037] The current is input into the rectifier and the optimal voltage vector is output.

[0038] To achieve the above-mentioned purpose, the second embodiment of the present application proposes a robust model predictive control device based on inductance observation, comprising:

[0039] A calculation module, used for observing the inductor current error in real time using an inductor observer and calculating the observed inductor parameters;

[0040] A correction module, used for correcting the system prediction model according to the observed inductance parameter;

[0041] The output module is used to calculate the optimal voltage vector output of the power system according to the system prediction model.

[0042] To achieve the above-mentioned purpose, the third aspect of the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0043] The memory stores computer-executable instructions;

[0044] The processor executes the computer-executable instructions stored in the memory to implement the method as described in any one of the first aspects.

[0045] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present application proposes a computer-readable storage medium, in which computer-readable storage medium is stored computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the method as described in any one of the first aspects.

[0046] To achieve the above-mentioned purpose, the fifth aspect of the present application proposes a computer program product, which implements any method in the first aspect when executed by a processor.

[0047] The robust model predictive control method, device, electronic device and storage medium based on inductance observation provided by the present application analyze the prediction model error of Vienna rectifier and reveal the influence mechanism of the prediction model on system performance under inductance parameter mismatch. Then, based on the control error model, an inductance observer is constructed to observe and correct the prediction model parameters, eliminating the steady-state error of the prediction model caused by inductance parameter mismatch.

[0048] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0050] Figure 1A flowchart of a robust model predictive control method based on inductance observation provided in an embodiment of the present application;

[0051] Figure 2 is a schematic diagram of the inductance observer structure;

[0052] Figure 3 This is the control block diagram of Vienna rectifier RMPC strategy;

[0053] Figure 4 It is a schematic diagram of the grid-side three-phase waveform and the THD spectrum analysis results of the a-phase current when the Vienna rectifier OSS-MPC strategy is controlled under different inductance mismatches;

[0054] Figure 5 Schematic diagram of input current waveform and harmonic content of a-phase current under Vienna rectifier RMPC;

[0055] Figure 6 It is a schematic diagram of the harmonic content of phase a current under different inductance parameter mismatch;

[0056] Figure 7 Schematic diagram of steady-state waveform and harmonic content under FCS-MPC algorithm control;

[0057] Figure 8 Schematic diagram of steady-state waveform and harmonic content under OSS-MPC algorithm control;

[0058] Fig. 9 Schematic diagram of steady-state waveform and harmonic content under RMPC algorithm control;

[0059] Fig.10 A schematic diagram of the structure of a robust model predictive control device based on inductance observation provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0061] As a three-level AC / DC converter, Vienna has been widely used in aerospace power supplies, car charging piles, data centers, wind power generation systems and other fields due to its simple topology, no need to consider switching dead zone, high power density, small power tube voltage stress and low grid-connected current harmonic content.

[0062] In view of the excellent performance of Vienna rectifier and its wide application potential, and the fact that its performance mainly depends on the effectiveness of the control strategy, a large number of literatures have conducted in-depth research on its control strategy, aiming to improve the performance of Vienna, which is of great significance and value to broadening its application field and improving power quality.

[0063] At present, the control strategies of Vienna rectifier mainly focus on double closed-loop PI control, hysteresis current control, single cycle control, sliding film control, model predictive control and other aspects. The double closed-loop PI is introduced into Vienna control, and good results are achieved. However, PI controllers always have problems such as high system accuracy requirements, slow response speed, sensitivity to system parameters, and difficulty in ensuring stability. Hysteresis current control is applied to Vienna, and midpoint potential balance and other effects are achieved. However, the system frequency is prone to be unstable, and the current error and stability of the system are limited by the loop width. Single cycle control is introduced into Vienna rectifier and excellent results are achieved. However, this control strategy requires rapid reset of sampling and integrator, and the parameters of the control circuit need to be adjusted continuously. In addition, if the control loop contains midpoint potential constraints, the harmonic content of the system input current will increase significantly. The sliding mode variable structure control method is applied to Vienna rectifier, which successfully solves the problem of slow dynamic response of traditional PI control and achieves good results. However, due to the discontinuous switching of Vienna rectifier and the difficulty in selecting sliding mode coefficients, the system is prone to jitter, which affects stability. Model predictive control (MPC) has the characteristics of multivariable, high precision, fast response, and strong adaptability, and is widely used in chemical industry, smelting, power system and other fields. Many existing studies have used model predictive control in converters and solved several problems. According to the optimization method, model predictive control is divided into continuous set model predictive control (CCS-MPC) and finite set model predictive control (FCS-MPC). Compared with CCS-MPC, FCS-MPC has the advantages of simple control structure, fast dynamic response, support for multi-objective control, and easy processing of nonlinear constraints. It is widely used in converter control. Although the FCS-MPC algorithm is superior to traditional control strategies in many aspects, it also faces some shortcomings in practical applications, such as non-fixed switching frequency, large amount of calculation, high total harmonic distortion of input current, and dependence of system control accuracy on the accuracy of model parameters.

[0064] To address this problem, the present invention provides a robust model predictive control method based on inductance observation. Figure 1 The following is a flow chart of a robust model predictive control method based on inductance observation provided in an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0065] Step 101, using an inductor observer to observe the inductor current error in real time and calculate the observed inductor parameters;

[0066] Step 102, calibrating the system prediction model according to the observed inductance parameter;

[0067] Step 103: Calculate the optimal voltage vector output of the power system according to the system prediction model.

[0068] In this embodiment, the control accuracy of the model prediction depends to a large extent on the accuracy of the prediction model. Changes in the model parameters will cause the prediction model to change, thereby reducing the performance of the model predictive control (MPC). However, the Vienna rectifier under actual operating conditions is affected by many factors such as temperature and current, and the actual inductance parameters will change. If the inductance parameters of the prediction model do not match the actual inductance parameters, the control accuracy of the prediction model will decrease, thereby reducing the quality of the input current.

[0069] The prediction model can be obtained by discretizing the continuous space state model of the Vienna rectifier. Select the prediction time domain as 1 and set the system sampling period as T, then the forward Euler discretization formula is:

[0070]

[0071] The discretized current prediction model of Vienna rectifier can be expressed as:

[0072]

[0073] In the formula, i α,β (k+1) represents the predicted values ​​of α and β axis currents at time k+1 respectively; e α,β (k) represents the measured voltage values ​​of the α and β axes at time k; u α,β (k) represents the components of the optimal voltage vector output at time k on the α and β axes respectively; i α,β (k) respectively represent the α and β axis current measurement values ​​at time k. As can be seen from formula (2), by substituting the input current value, voltage value and alternative voltage vector at the current sampling time into the prediction model, the current prediction value at sampling time k+1 can be obtained.

[0074] By analyzing the steady-state error caused by the inductance parameters, the current change in the αβ coordinate system within a control cycle can be obtained through equation (2):

[0075]

[0076] In the formula, Δi α and Δi βThey represent the components of the input current change in one control cycle on the α and β axes. Ignoring the influence of the resistance parameter change, when the actual inductance value L is larger than the predicted model inductance value L, Δi α , Δi β On the contrary, when the actual inductance value L is smaller than the predicted model inductance value L, Δi α , Δi β It can be seen that the size of the inductance parameter directly affects the performance of the control algorithm current tracking.

[0077] Assuming the inductance error is ΔL, the actual inductance value of the system is L m It can be expressed as:

[0078] L m =L+ΔL(4)

[0079] According to equations (2) and (4), the current prediction value under actual inductance parameter control can be obtained as follows:

[0080]

[0081] By combining equation (2) and equation (5), we can obtain the objective function with current control error as the constraint term when the inductor parameters are mismatched:

[0082]

[0083] The current error of the voltage vector corresponding to each switch state in the sector is calculated by equation (6), and then the current control error is obtained by rolling optimization and selecting the minimum value. However, the process is computationally intensive, and the voltage vector position has a large range of variation, and its value is relatively complex. Therefore, the current control error can be converted into an equivalent voltage vector error. Substituting equations (2) and (4) into equation (6) respectively, we can obtain:

[0084]

[0085] In the formula, ΔLT[e α (k)-Ri α (k)] 2 and ΔLT[e β (k)-Ri β (k)] 2 It is a constant at time k, so equation (7) is equivalent to:

[0086]

[0087] make:

[0088]

[0089]

[0090] Formula (8) can be simplified as:

[0091]

[0092] The current error can be transformed into an equivalent voltage vector error by using equation (10). The following discusses α and e β The value range of the control target current vector at time k+1 can be obtained by the PI controller. Therefore, when the control target current is known, the control target voltage vector at time k can be obtained through the mathematical model in the αβ coordinate system:

[0093]

[0094] The grid voltage e(k), grid current i(k), voltage vector u(k) and predicted current i m The component of (k+1) in the αβ coordinate system is expressed in complex form:

[0095]

[0096] In the formula, e m and i m are the rated voltage and peak current of the power grid respectively; θ is the rotation angle of the voltage vector in the αβ coordinate system. Under the unity power factor operation, the voltage and current are in phase; i max m is the maximum predicted current value of the next control cycle; Δi max The maximum current change allowed within one control cycle, usually 10% of the rated current.

[0097] Since the filter resistance R is small, it can be ignored. Substituting equation (12) into equation (11), the maximum voltage vector that meets the control requirements can be obtained:

[0098]

[0099] When the control period T is small enough, equation (13) can be approximated as:

[0100]

[0101] Therefore, the maximum voltage vector can be expressed as:

[0102]

[0103] Where A represents the modulus length of the maximum voltage vector, It represents the phase angle of the maximum voltage vector in the αβ coordinate system, and the calculation formula is:

[0104]

[0105] The maximum voltage vector should satisfy the feasible region constraints of the Vienna rectifier topology. If the value is too large, the target voltage vector will exceed the operational domain. Therefore, in order to achieve better control effect, the phase angle should be guaranteed. If the angle is greater than -30°, the modulus A should be less than the DC bus voltage. Based on the geometric relationship and combined with equations (9) and (11), the value range of the component of the maximum error of the voltage vector in the αβ coordinate system is:

[0106]

[0107] Get the error e of the target voltage vector in the αβ coordinate system α and e β After that, it is converted into current control error. According to equations (9) and (11), the optimal reference voltage vector can be obtained as:

[0108]

[0109] The actual prediction model of the controlled current is:

[0110]

[0111] Substitute formula (18) into formula (19):

[0112]

[0113] Then the current control error within one control cycle can be expressed as:

[0114]

[0115] Since the first and third terms in equation (21) are both related to T / L, these two errors can be eliminated by adjusting the prediction period T and designing the inductance parameter L. The control period is equivalent to a period of actual control. The prediction period is equivalent to the prediction period, which is a prediction value. When analyzing, the relationship between inductance and period is obtained by looking at these two periods, and then the inductance parameters are affected by changing the prediction period. Ideally, it can be approximately equivalent to:

[0116]

[0117] From formula (12), we can know that:

[0118]

[0119] make:

[0120]

[0121] but:

[0122]

[0123] When the predicted inductance value is equal to the actual inductance value, equation (21) can be simplified to:

[0124]

[0125] Therefore, by adjusting the value of T / L, the stable control of the current can be achieved. At the same time, according to formula (27), the range of inductance parameter values ​​that meet the control requirements can be obtained, solving the problem of difficult inductance parameter design. Formula (27) is:

[0126]

[0127] Based on the above analysis, the control error of the model predictive control strategy is affected by the system control cycle and inductance parameters, and the mismatch of inductance parameters will lead to an increase in the control error. Therefore, it is necessary to use an inductance observer to observe and correct the inductance parameters of the system prediction model.

[0128] In order to deal with the inductor parameter mismatch problem during the operation of Vienna rectifier, a robust model predictive control strategy based on inductor observation is studied. The inductor current error is observed in real time by the inductor observer and the observed inductor parameters are calculated. The system prediction model is corrected using the observed inductor parameters to ensure the selection of the optimal voltage vector output during operation.

[0129] The actual inductance value and ideal inductance value of the power system are obtained. The resistance in the prediction model is the same as the actual resistance of the power system. The actual current prediction value of the next control cycle is calculated based on the actual inductance value, control cycle length, voltage measurement value, current measurement value, and optimal voltage vector. Assuming that the actual inductance value of the system is L m , and the resistance in the prediction model is the same as the actual resistance of the system, then the actual predicted current of the system in the next control cycle can be obtained from formula (5):

[0130]

[0131] Among them, i α m (k+1) is the α component of the actual current prediction value, L m is the actual inductance value, T is the control cycle length, e α (k) is the α component of the voltage measurement value, i α (k) is the α component of the current measurement value, u α (k) is the α component of the optimal voltage vector; i β m(k+1) is the β component of the actual current prediction value, L m is the actual inductance value, T is the control cycle length, e β (k) is the β component of the voltage measurement value, i β (k) is the β component of the current measurement value, u β (k) is the β component of the optimal voltage vector;

[0132] The ideal current prediction value of the next control cycle is calculated according to the ideal inductance value, the control cycle length, the voltage measurement value, the current measurement value, and the optimal voltage vector, and the formula is:

[0133]

[0134] Among them, i α (k+1) is the α component of the ideal current prediction value, L is the ideal inductance value, T is the control cycle length, R is the filter resistance, e α (k) is the α component of the voltage measurement value, i α (k) is the α component of the current measurement value, u α (k) is the α component of the optimal voltage vector; i β (k+1) is the β component of the ideal current prediction value, L is the ideal inductance value, T is the control cycle length, e β (k) is the β component of the voltage measurement value, i β (k) is the β component of the current measurement value, u β (k) is the β component of the optimal voltage vector;

[0135] Divide the expressions of α component and β component correspondingly to obtain the observed value L of the inductance parameter at the next moment o for:

[0136]

[0137] Optionally, the method further includes:

[0138] Calculate the inductance observation value at the current moment, the formula is as follows:

[0139]

[0140] Optionally, the step 102 of correcting the system prediction model according to the observed inductance parameter includes:

[0141] The inductance observation value at the current moment is processed by a limiter and low-pass filtered to obtain a target inductance observation value;

[0142] The target inductance observation value is used to update the inductance observation value in the system prediction model.

[0143] In this embodiment, after adding a limiter and a low-pass filter to filter out unnecessary high-frequency components, the observed inductance value can be used to correct the prediction model, thereby ensuring that the optimal voltage vector output is selected during operation. Figure 2 shown.

[0144] Optionally, the step 103 of calculating the optimal voltage vector output of the power system according to the system prediction model includes:

[0145] The ideal current prediction value of the next control cycle is calculated according to the target inductance observation value, the control cycle length, the voltage measurement value, the current measurement value, and the optimal voltage vector. The formula is:

[0146]

[0147] L b is the target inductance observation value.

[0148] Optionally, the step 103 of calculating the optimal voltage vector output of the power system according to the system prediction model includes:

[0149] According to the ideal current prediction value and actual current value of the next control cycle, the objective function with the current control error as the constraint term is determined:

[0150] E i =[i α m (k+1)-i α (k+1)] 2 +[i β m (k+1)-i β (k+1)] 2 (33)

[0151] Among them, E i To control the error;

[0152] calculating a duty cycle according to the control error;

[0153] The current is input into the rectifier and the optimal voltage vector is output.

[0154] In this embodiment, the Vienna rectifier RMPC strategy control block diagram is as follows: Figure 3 The prediction model is modified by the inductance observer, and the inductance parameters of the prediction model are corrected in real time, which improves the robustness of the algorithm to the inductance parameters and ensures that the Vienna rectifier system can operate well under the inductance parameter mismatch.

[0155] In order to verify the effectiveness of the above theoretical analysis, a simulation model is built in MATLAB / Simulink, and the proposed RMPC strategy is simulated and verified. At the same time, a comparative analysis of the input current quality is performed with the OSS-MPC control strategy. The parameters of the simulation platform are shown in Table 1.

[0156] Table 1 Vienna rectifier simulation parameters

[0157]

[0158] Figure 4 The following is a schematic diagram of the grid-side three-phase waveform and a-phase current THD spectrum analysis results when the Vienna rectifier OSS-MPC strategy is controlled under different inductance mismatches. Figure 4 (a) It can be seen that when the predicted inductance L is 1.5mH, the harmonic content of phase a current is 6.54%; Figure 4 (b) It can be seen that when the predicted inductance L is 6mH, the harmonic content THD of the a-phase current is 6.80%. The results show that when the system inductance parameters mismatch with the inductance parameters in the prediction model, the prediction model generates control errors, which in turn affects the control accuracy of the OSS-MPC strategy on the current and reduces the quality of the grid-connected current.

[0159] Inductor parameter mismatch (actual inductance L m When the inductance L of the prediction model is 3mH and 1.5mH and 6mH respectively), the input current waveform and harmonic content of the a-phase current of the Vienna rectifier under RMPC are shown in the figure below: Figure 5 As shown. Figure 5 As shown in (a), when the predicted inductance parameter L is 1.5mH, the harmonic content THD of phase a current is 2.72%; Figure 5 As shown in (b), when the predicted inductance parameter L is 6mH, the harmonic content THD of phase a current is 2.18%. The results show that the proposed RMPC strategy can effectively suppress the increase of current harmonic content caused by inductance parameter mismatch.

[0160] The inductance parameter offset coefficient G can be defined as the ratio of the actual inductance parameter to the predicted inductance parameter. To verify the robustness of the proposed RMPC algorithm, the harmonic content of phase a current under different inductance parameter mismatches was experimentally measured. Figure 6 Figure 1 is a schematic diagram of the harmonic content of phase a current under different inductance parameter mismatch. Figure 6 It can be seen that when the inductance parameter offset G varies in the range of 0.3 to 7, the harmonic content of the a-phase current can be maintained below 4.5%, meeting the harmonic requirements of the power grid. The results show that the proposed RMPC algorithm can effectively control the input current to maintain a low harmonic content within the given inductance parameter mismatch range, and has good robustness.

[0161] In order to verify the effectiveness of the RMPC strategy based on inductance observation proposed in this article, an experimental platform was built. The platform parameters are consistent with the above simulation parameters.

[0162] In order to verify the effectiveness of the proposed RMPC control strategy when the inductor parameters are mismatched, Figure 7 , Figure 8 , Fig. 9 Schematic diagrams of steady-state waveforms and harmonic content under the control of the FCS-MPC algorithm, OSS-MPC algorithm and RMPC algorithm are given respectively (in order to create an experimental environment for inductance parameter mismatch, the inductance parameter of the predicted model is set to 1.5mH, and the actual inductance is still 3mH).

[0163] When the inductance parameters in the prediction model are different from the actual inductance parameters, the input current under the control of the FCS-MPC algorithm is significantly distorted, and the harmonic content is 17.0% at this time; the current distortion under the control of the OSS-MPC algorithm is reduced, because the OSS-MPC uses the voltage vector sequence to synthesize the reference voltage vector, and the control error is reduced, but the harmonic content is still 7.3% at this time; the current harmonic content under the control of the proposed RMPC algorithm is only 2.9%, and the current is not significantly distorted. The experimental results show that the RMPC algorithm can effectively eliminate the control error caused by the mismatch of the inductance parameters, thereby improving the robustness of the algorithm to the inductance parameters, and thus ensuring the quality of the input current.

[0164] In order to implement the above embodiment, the present application also proposes a robust model predictive control device based on inductance observation. Fig.10 A schematic diagram of the structure of a robust model predictive control device based on inductance observation provided in an embodiment of the present application. Fig.10 As shown, the device comprises:

[0165] A calculation module 1010, used to observe the inductor current error in real time using an inductor observer and calculate the observed inductor parameters;

[0166] A correction module 1020, configured to correct the system prediction model according to the observed inductance parameter;

[0167] The output module 1030 is used to calculate the optimal voltage vector output of the power system according to the system prediction model.

[0168] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0169] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0170] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0171] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0172] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0173] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0174] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0175] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0176] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0177] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0178] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0179] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0180] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0181] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A robust model predictive control method based on inductance observation, characterized in that: The following steps are involved: The inductor current error is observed in real time by using an inductor observer and the observed inductor parameters are calculated; Correcting the system prediction model according to the observed inductance parameter; The optimal voltage vector output of the power system is calculated according to the system prediction model.

2. The method according to claim 1, characterized in that The method of using an inductor observer to observe the inductor current error in real time and calculate the observed inductor parameter includes: The actual inductance value and the ideal inductance value of the power system are obtained. The resistance in the prediction model is the same as the actual resistance of the power system. The actual current prediction value of the next control cycle is calculated according to the actual inductance value, the control cycle length, the voltage measurement value, the current measurement value, and the optimal voltage vector. The formula is: Among them, i α m (k+1) is the α component of the actual current prediction value, L m is the actual inductance value, T is the control cycle length, e α (k) is the α component of the voltage measurement value, i α (k) is the α component of the current measurement value, u α (k) is the α component of the optimal voltage vector; i β m (k+1) is the β component of the actual current prediction value, L m is the actual inductance value, T is the control cycle length, e β (k) is the β component of the voltage measurement value, i β (k) is the β component of the current measurement value, u β (k) is the β component of the optimal voltage vector; The ideal current prediction value of the next control cycle is calculated according to the ideal inductance value, the control cycle length, the voltage measurement value, the current measurement value, and the optimal voltage vector, and the formula is: Among them, i α (k+1) is the α component of the ideal current prediction value, L is the ideal inductance value, T is the control cycle length, R is the filter resistance, e α (k) is the α component of the voltage measurement value, i α (k) is the α component of the current measurement value, u α (k) is the α component of the optimal voltage vector; i β (k+1) is the β component of the ideal current prediction value, L is the ideal inductance value, T is the control cycle length, e β (k) is the β component of the voltage measurement value, i β (k) is the β component of the current measurement value, u β (k) is the β component of the optimal voltage vector; Divide the expressions of α component and β component correspondingly to obtain the observed value L of the inductance parameter at the next moment o for:

3. The method according to claim 2, characterized in that The method further comprises: Calculate the inductance observation value at the current moment, the formula is as follows:

4. The method according to claim 3, characterized in that The correcting the system prediction model according to the observed inductance parameter comprises: The inductance observation value at the current moment is processed by a limiter and low-pass filtered to obtain a target inductance observation value; The target inductance observation value is used to update the inductance observation value in the system prediction model.

5. The method according to claim 4, characterized in that The step of calculating the optimal voltage vector output of the power system according to the system prediction model includes: The ideal current prediction value of the next control cycle is calculated according to the target inductance observation value, the control cycle length, the voltage measurement value, the current measurement value, and the optimal voltage vector. The formula is: L b is the target inductance observation value.

6. The method according to claim 1, characterized in that The step of calculating the optimal voltage vector output of the power system according to the system prediction model includes: According to the ideal current prediction value and actual current value of the next control cycle, the objective function with the current control error as the constraint term is determined: Among them, E i To control the error; calculating a duty cycle according to the control error; The current is input into the rectifier and the optimal voltage vector is output.

7. A robust model predictive control device based on inductance observation, characterized in that: include: A calculation module, used for observing the inductor current error in real time using an inductor observer and calculating the observed inductor parameters; A correction module, used for correcting the system prediction model according to the observed inductance parameter; The output module is used to calculate the optimal voltage vector output of the power system according to the system prediction model.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

Images